Research Findings

Police Presence and Crime

Would our cities be safer if there are more police officers on the streets? Preventive patrol is one of the main functions of the police, and it would be important to know whether and by how much additional police patrol would decrease crime.

Although common wisdom predicts that more police would decrease crime, an empirical analysis on the causal effect of police on crime is a lot more challenging than one might think. The main concern is that the level of police presence may depend on the level of local crime. Bigger police departments that send out more officers on the patrol may be doing it because of high crime rates. In this case, the observed relationship between police presence and crime necessarily confounds the effect of police on crime and the effect of crime on police. It is not clear how we can successfully disentangle these two effects.

Fortunately, economists found a few instances where the decision to increase police presence on streets had little to do with underlying local crime trends, and used this “clean” variation in police presence to estimate the causal effect of police on crime. The first example comes from a terror alert system in the United States. Between 2003 and 2011, the U.S. Office of Homeland Security introduced a five-color terror alert system which assigned different colors to different levels of terrorism risk, ranging from green (low), blue (general), yellow (elevated), orange (high), and red (severe). (You may have seen the color-coded sign yourself if you visited a U.S. airport during this period.)

Economists Jonathan Klick and Alex Tabarrok noted that all federal agencies (including police) had to adjust their anti-terrorism efforts according to the current level of the terror alert, and investigated how an increase in police presence caused by a higher terror alert affected crime in Washington D.C. In particular, whenever the alert level moved from yellow (elevated) to orange (high), the Washington D.C. Police sharply increased its visible presence in the National Mall area, where numerous federal government buildings (most notably, the White House and Capitol Hill) were located.

It is important to emphasize that the alert level depended on the level of terrorism risk at the national level, and had little to do with local crime trends in Washington D.C. Other crime-relevant local factors, such as local demographic shifts, labor market conditions, poverty and economic inequality could not influence the changes in terror alerts either.

And this is precisely why we can plausibly attribute the difference in crime between high-alert and low-alert periods to the increased police presence. Klick and Tabarrok’s regression results show that the number of crimes in the National Mall area fell by 15 percent during a high-alert period compared to a low-alert period. On the other hand, they found little difference in crime between high- and low-alert periods in other parts of Washington D.C., where a high terror alert did not lead to a significant increase in police presence.

Other researchers also used a terrorism-driven variation in police presence to find the causal effect of police presence on crime. Economists Rafael Di Tella and Ernesto Schargrodsky noted that a terrorist attack on a Jewish center in Buenos Aires in 1994 led to an immediate surge in police presence near all Jewish institutions (schools, synagogues, etc.) in Argentina. Mirko Draca, Stephen Machin, and Robert Witt show that police presence in Central London sharply increased following the 2005 terrorist attacks in London. In both cases, researchers found that increased police presence led to a significant decrease in local crime rates.

Police Force Size and Crime

Similarly, economists look for a “clean” variation in the size of police force (which has little to do with other crime-relevant factors) in order to estimate the causal effect of police force size on crime. But when does the size of a local police force increase or decrease for reasons unrelated to local crime rates and other crime-relevant factors?

A major policy reform by national government can be one example. For example, the U.S. Congress passed the Violent Crime Control and Law Enforcement Act in 1994, providing grants totaling $8.8 billion to local police agencies so that they could hire more officers. The funding was then used to hire 88,000 additional police officers (roughly a 14 percent increase) across the U.S.

Economists William Evans and Emily Owens found that this grant-driven increase in the number of police officers led to a significant decrease in crime: A 10 percent increase in the size of the police force led to a 10 percent reduction in violent crimes and a 2.6 percent reduction in property crimes. (Importantly, as we saw from Week, 2, they use a within-unit comparison. A 10 percent increase in the size of the police force within a city is associated with a 10 percent reduction in violent crimes within the city.)

Stephen Machin and Olivier Marie also investigated the effect of a similar police policy intervention in the U.K. In 2002, the U.K. government introduced the Street Crime Initiative, which provided approximately £48 million to 10 out of the 43 local police forces in the U.K. to pay for additional hires, overtime payment, and acquisition of new information technology. And not surprisingly, they found that robbery rates in the grant-receiving areas significantly fell compared to the non-receiving areas following the introduction of the Initiative.

Importance of an “Identifying” Variation

Suppose we collect all the data available on crime and police and run a regression analysis. We will obtain some number that tells us how crime and police are correlated, but we would not view this as a causal effect of police on crime. The usual worry is that the difference in police presence across places and over time may have been driven by local crime trends (reverse causality) or other crime-relevant factors (self-selection).

On the other hand, the studies discussed above successfully identified cases in which the variations in the level of police presence had little to do with local crime trends and other crime-relevant factors. We call this an identifying variation, because it allows us to identify the causal effect of interest. Of course, some identifying variations are stronger than the other, in the sense that the large variation in the level of police plausibly had very little to do with local crime trends and other crime-relevant factors. And using a strong identifying variation leads to a strong empirical research design. (Out of the three identifying variations introduced in this article, namely, an increased level of terror-alert, a terrorist attack, a large national government grant for additional police hires, which one do you think is a stronger identifying variation? Which one is weaker?)

In these studies, we will still run similar regressions as before, using panel data and fixed-effect regression to compare the change in crime rates within the same unit. But with a strong identifying variation, this change in crime rates can now be plausibly interpreted as a causal effect of interest. In other words, the main strength of these studies is not that they use more sophisticated statistical methods than what we already saw. Rather, it is that they successfully identified and used good identifying variations, making their regression results bear a causal interpretation.